renaissance-movie-lens_0
[2023-04-19T09:50:46.963Z] Running test renaissance-movie-lens_0 ...
[2023-04-19T09:50:46.963Z] ===============================================
[2023-04-19T09:50:46.963Z] renaissance-movie-lens_0 Start Time: Wed Apr 19 10:50:46 2023 Epoch Time (ms): 1681897846919
[2023-04-19T09:50:46.963Z] variation: NoOptions
[2023-04-19T09:50:46.963Z] JVM_OPTIONS:
[2023-04-19T09:50:46.963Z] { \
[2023-04-19T09:50:46.963Z] echo ""; echo "TEST SETUP:"; \
[2023-04-19T09:50:46.963Z] echo "Nothing to be done for setup."; \
[2023-04-19T09:50:46.963Z] mkdir -p "/Users/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_mac/aqa-tests/TKG/../TKG/output_16818969736089/renaissance-movie-lens_0"; \
[2023-04-19T09:50:46.963Z] cd "/Users/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_mac/aqa-tests/TKG/../TKG/output_16818969736089/renaissance-movie-lens_0"; \
[2023-04-19T09:50:46.963Z] echo ""; echo "TESTING:"; \
[2023-04-19T09:50:46.963Z] "/Users/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_mac/openjdkbinary/j2sdk-image/Contents/Home/bin/..//bin/java" -jar "/Users/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_mac/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/Users/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_mac/aqa-tests/TKG/../TKG/output_16818969736089/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2023-04-19T09:50:46.963Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /Users/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_mac/aqa-tests/TKG/..; rm -f -r "/Users/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_mac/aqa-tests/TKG/../TKG/output_16818969736089/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2023-04-19T09:50:46.963Z] echo ""; echo "TEST TEARDOWN:"; \
[2023-04-19T09:50:46.964Z] echo "Nothing to be done for teardown."; \
[2023-04-19T09:50:46.964Z] } 2>&1 | tee -a "/Users/jenkins/workspace/Test_openjdk8_hs_extended.perf_x86-64_mac/aqa-tests/TKG/../TKG/output_16818969736089/TestTargetResult";
[2023-04-19T09:50:46.964Z]
[2023-04-19T09:50:46.964Z] TEST SETUP:
[2023-04-19T09:50:46.964Z] Nothing to be done for setup.
[2023-04-19T09:50:46.964Z]
[2023-04-19T09:50:46.964Z] TESTING:
[2023-04-19T09:50:51.001Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2023-04-19T09:50:54.587Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2023-04-19T09:50:57.059Z] Got 100004 ratings from 671 users on 9066 movies.
[2023-04-19T09:50:57.059Z] Training: 60056, validation: 20285, test: 19854
[2023-04-19T09:50:57.059Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2023-04-19T09:50:57.059Z] GC before operation: completed in 206.297 ms, heap usage 231.102 MB -> 27.024 MB.
[2023-04-19T09:51:04.733Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:51:07.916Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:51:11.990Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:51:16.055Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:51:17.953Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:51:19.768Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:51:22.206Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:51:24.017Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:51:24.383Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T09:51:24.383Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:51:24.750Z] Movies recommended for you:
[2023-04-19T09:51:24.750Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:51:24.750Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:51:24.750Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (27583.526 ms) ======
[2023-04-19T09:51:24.750Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2023-04-19T09:51:25.117Z] GC before operation: completed in 314.360 ms, heap usage 1021.445 MB -> 53.960 MB.
[2023-04-19T09:51:29.200Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:51:32.384Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:51:35.576Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:51:38.759Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:51:41.275Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:51:43.080Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:51:44.888Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:51:46.694Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:51:47.061Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T09:51:47.061Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:51:47.426Z] Movies recommended for you:
[2023-04-19T09:51:47.426Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:51:47.426Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:51:47.426Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (22254.935 ms) ======
[2023-04-19T09:51:47.426Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2023-04-19T09:51:47.426Z] GC before operation: completed in 242.465 ms, heap usage 457.510 MB -> 43.469 MB.
[2023-04-19T09:51:51.481Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:51:54.678Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:51:57.862Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:52:01.058Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:52:03.038Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:52:04.852Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:52:06.691Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:52:08.494Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:52:08.860Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T09:52:08.860Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:52:09.230Z] Movies recommended for you:
[2023-04-19T09:52:09.230Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:52:09.230Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:52:09.230Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (21564.000 ms) ======
[2023-04-19T09:52:09.230Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2023-04-19T09:52:09.230Z] GC before operation: completed in 158.908 ms, heap usage 323.604 MB -> 43.698 MB.
[2023-04-19T09:52:12.786Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:52:15.244Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:52:18.432Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:52:21.739Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:52:23.546Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:52:25.358Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:52:27.166Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:52:28.982Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:52:28.982Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T09:52:28.982Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:52:28.982Z] Movies recommended for you:
[2023-04-19T09:52:28.982Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:52:28.982Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:52:28.982Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (19802.638 ms) ======
[2023-04-19T09:52:28.982Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2023-04-19T09:52:29.637Z] GC before operation: completed in 158.221 ms, heap usage 97.808 MB -> 49.680 MB.
[2023-04-19T09:52:32.817Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:52:36.005Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:52:39.211Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:52:41.658Z] RMSE (validation) = 1.0045407704488025 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:52:44.092Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:52:45.899Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:52:47.713Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:52:49.516Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:52:49.884Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T09:52:49.885Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:52:49.885Z] Movies recommended for you:
[2023-04-19T09:52:49.885Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:52:49.885Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:52:49.885Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (20794.888 ms) ======
[2023-04-19T09:52:49.885Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2023-04-19T09:52:50.337Z] GC before operation: completed in 143.520 ms, heap usage 141.114 MB -> 46.026 MB.
[2023-04-19T09:52:52.778Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:52:55.984Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:52:59.177Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:53:01.614Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:53:03.414Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:53:06.961Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:53:07.350Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:53:09.195Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:53:09.195Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T09:53:09.195Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:53:09.195Z] Movies recommended for you:
[2023-04-19T09:53:09.195Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:53:09.195Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:53:09.195Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (19213.066 ms) ======
[2023-04-19T09:53:09.195Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2023-04-19T09:53:09.586Z] GC before operation: completed in 146.326 ms, heap usage 144.723 MB -> 43.570 MB.
[2023-04-19T09:53:12.759Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:53:15.942Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:53:19.179Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:53:21.693Z] RMSE (validation) = 1.0045407704488025 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:53:23.506Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:53:25.310Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:53:27.747Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:53:29.569Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:53:29.569Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-19T09:53:29.569Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:53:29.569Z] Movies recommended for you:
[2023-04-19T09:53:29.569Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:53:29.569Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:53:29.569Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (20146.130 ms) ======
[2023-04-19T09:53:29.569Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2023-04-19T09:53:29.947Z] GC before operation: completed in 165.380 ms, heap usage 1.323 GB -> 57.192 MB.
[2023-04-19T09:53:33.123Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:53:36.304Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:53:39.481Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:53:43.660Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:53:44.050Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:53:45.884Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:53:47.744Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:53:49.551Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:53:49.916Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T09:53:49.916Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:53:49.916Z] Movies recommended for you:
[2023-04-19T09:53:49.916Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:53:49.916Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:53:49.916Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (20117.011 ms) ======
[2023-04-19T09:53:49.916Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2023-04-19T09:53:49.916Z] GC before operation: completed in 139.350 ms, heap usage 1.350 GB -> 60.062 MB.
[2023-04-19T09:53:53.459Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:53:56.663Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:53:59.959Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:54:02.423Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:54:04.864Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:54:06.667Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:54:08.476Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:54:10.925Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:54:10.925Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-19T09:54:10.925Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:54:10.925Z] Movies recommended for you:
[2023-04-19T09:54:10.925Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:54:10.925Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:54:10.925Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (20964.509 ms) ======
[2023-04-19T09:54:10.925Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2023-04-19T09:54:11.290Z] GC before operation: completed in 204.796 ms, heap usage 1.249 GB -> 53.777 MB.
[2023-04-19T09:54:14.490Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:54:17.667Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:54:20.861Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:54:24.031Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:54:25.653Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:54:27.451Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:54:29.262Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:54:31.077Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:54:31.077Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T09:54:31.077Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:54:31.444Z] Movies recommended for you:
[2023-04-19T09:54:31.444Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:54:31.444Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:54:31.444Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (20031.909 ms) ======
[2023-04-19T09:54:31.444Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2023-04-19T09:54:31.444Z] GC before operation: completed in 187.065 ms, heap usage 1.326 GB -> 52.320 MB.
[2023-04-19T09:54:34.634Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:54:37.814Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:54:41.202Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:54:44.426Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:54:46.259Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:54:47.538Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:54:50.032Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:54:51.839Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:54:51.839Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T09:54:51.839Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:54:52.206Z] Movies recommended for you:
[2023-04-19T09:54:52.206Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:54:52.206Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:54:52.206Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (20653.013 ms) ======
[2023-04-19T09:54:52.206Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2023-04-19T09:54:52.206Z] GC before operation: completed in 132.049 ms, heap usage 147.065 MB -> 57.110 MB.
[2023-04-19T09:54:55.401Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:54:57.851Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:55:01.903Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:55:04.346Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:55:06.156Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:55:07.972Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:55:10.413Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:55:12.239Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:55:12.239Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-19T09:55:12.239Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:55:12.239Z] Movies recommended for you:
[2023-04-19T09:55:12.239Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:55:12.239Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:55:12.239Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (20144.546 ms) ======
[2023-04-19T09:55:12.239Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2023-04-19T09:55:12.607Z] GC before operation: completed in 158.350 ms, heap usage 257.909 MB -> 44.342 MB.
[2023-04-19T09:55:15.075Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:55:18.449Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:55:21.633Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:55:24.822Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:55:26.100Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:55:27.926Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:55:30.402Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:55:32.221Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:55:32.221Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T09:55:32.221Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:55:32.221Z] Movies recommended for you:
[2023-04-19T09:55:32.221Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:55:32.221Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:55:32.221Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (19760.262 ms) ======
[2023-04-19T09:55:32.221Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2023-04-19T09:55:32.588Z] GC before operation: completed in 141.439 ms, heap usage 1.243 GB -> 51.773 MB.
[2023-04-19T09:55:35.770Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:55:38.962Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:55:41.404Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:55:44.574Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:55:45.827Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:55:48.268Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:55:49.528Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:55:51.977Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:55:51.977Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T09:55:51.977Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:55:51.977Z] Movies recommended for you:
[2023-04-19T09:55:51.977Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:55:51.977Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:55:51.977Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (19527.297 ms) ======
[2023-04-19T09:55:51.977Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2023-04-19T09:55:51.977Z] GC before operation: completed in 134.183 ms, heap usage 1.241 GB -> 58.992 MB.
[2023-04-19T09:55:55.215Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:55:58.396Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:56:01.706Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:56:04.924Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:56:06.204Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:56:08.041Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:56:10.512Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:56:12.326Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:56:12.326Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-19T09:56:12.326Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:56:12.699Z] Movies recommended for you:
[2023-04-19T09:56:12.700Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:56:12.700Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:56:12.700Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (20397.869 ms) ======
[2023-04-19T09:56:12.700Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2023-04-19T09:56:12.700Z] GC before operation: completed in 180.770 ms, heap usage 122.719 MB -> 49.157 MB.
[2023-04-19T09:56:16.815Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:56:19.255Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:56:22.437Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:56:25.853Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:56:27.118Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:56:28.922Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:56:30.740Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:56:32.551Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:56:32.918Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T09:56:32.918Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:56:32.918Z] Movies recommended for you:
[2023-04-19T09:56:32.918Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:56:32.918Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:56:32.918Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (20269.575 ms) ======
[2023-04-19T09:56:32.918Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2023-04-19T09:56:32.918Z] GC before operation: completed in 122.569 ms, heap usage 115.174 MB -> 44.022 MB.
[2023-04-19T09:56:36.171Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:56:39.351Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:56:42.612Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:56:45.405Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:56:47.241Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:56:49.071Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:56:50.903Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:56:52.897Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:56:52.897Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T09:56:52.897Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:56:53.263Z] Movies recommended for you:
[2023-04-19T09:56:53.263Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:56:53.263Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:56:53.263Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (20056.882 ms) ======
[2023-04-19T09:56:53.263Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2023-04-19T09:56:53.263Z] GC before operation: completed in 155.844 ms, heap usage 1.247 GB -> 51.950 MB.
[2023-04-19T09:56:56.474Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:56:58.921Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:57:02.968Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:57:05.409Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:57:06.671Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:57:08.479Z] RMSE (validation) = 1.117495375711841 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:57:11.010Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:57:12.850Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:57:12.850Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949712.
[2023-04-19T09:57:12.850Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:57:13.217Z] Movies recommended for you:
[2023-04-19T09:57:13.217Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:57:13.217Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:57:13.217Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (19799.247 ms) ======
[2023-04-19T09:57:13.217Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2023-04-19T09:57:13.217Z] GC before operation: completed in 172.077 ms, heap usage 1.327 GB -> 52.625 MB.
[2023-04-19T09:57:16.407Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:57:19.584Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:57:22.834Z] RMSE (validation) = 1.310518969403201 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:57:25.277Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:57:27.353Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:57:28.640Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:57:30.802Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:57:32.091Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:57:32.482Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-19T09:57:32.482Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:57:32.482Z] Movies recommended for you:
[2023-04-19T09:57:32.482Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:57:32.482Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:57:32.482Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (19286.750 ms) ======
[2023-04-19T09:57:32.482Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2023-04-19T09:57:32.874Z] GC before operation: completed in 126.516 ms, heap usage 1.281 GB -> 52.022 MB.
[2023-04-19T09:57:36.102Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2023-04-19T09:57:39.286Z] RMSE (validation) = 2.1340923219297814 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2023-04-19T09:57:41.736Z] RMSE (validation) = 1.3105189694032011 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2023-04-19T09:57:44.913Z] RMSE (validation) = 1.0045407704488027 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2023-04-19T09:57:46.725Z] RMSE (validation) = 1.221833058605191 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2023-04-19T09:57:47.987Z] RMSE (validation) = 1.1174953757118413 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2023-04-19T09:57:50.503Z] RMSE (validation) = 0.9275717388537591 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2023-04-19T09:57:51.764Z] RMSE (validation) = 0.9001440970559653 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2023-04-19T09:57:52.132Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522617949711.
[2023-04-19T09:57:52.132Z] The best model improves the baseline by 14.43%.
[2023-04-19T09:57:52.132Z] Movies recommended for you:
[2023-04-19T09:57:52.132Z] WARNING: This benchmark provides no result that can be validated.
[2023-04-19T09:57:52.132Z] There is no way to check that no silent failure occurred.
[2023-04-19T09:57:52.132Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (19548.624 ms) ======
[2023-04-19T09:57:53.942Z] -----------------------------------
[2023-04-19T09:57:53.942Z] renaissance-movie-lens_0_PASSED
[2023-04-19T09:57:53.942Z] -----------------------------------
[2023-04-19T09:57:53.942Z]
[2023-04-19T09:57:53.942Z] TEST TEARDOWN:
[2023-04-19T09:57:53.942Z] Nothing to be done for teardown.
[2023-04-19T09:57:53.942Z] renaissance-movie-lens_0 Finish Time: Wed Apr 19 10:57:53 2023 Epoch Time (ms): 1681898273585